Systems and methods for determining actor status according to behavioral phenomena

ABSTRACT

Aspects relate to systems and methods for determining actor status according to behavioral phenomena. An exemplary system includes an eye sensor configured to detect an eye parameter as a function of an eye phenomenon, a speech sensor configured to detect a speech parameter as a function of a speech phenomenon, and a processor in communication with the eye sensor and the speech sensor; the processor is configured to receive the eye parameter and the speech parameter, determine an eye pattern as a function of the eye parameter, determine a speech pattern as a function of the speech parameter, and correlate one or more of the eye pattern and the speech pattern to a cognitive status.

FIELD OF THE INVENTION

The present invention generally relates to the field of diagnosing humanconditions. In particular, the present invention is directed to systemsand methods for determining actor status according to behavioralphenomena.

BACKGROUND

Poor cognitive states of an actor can hobble performance of high stressand/or high stakes responsibilities. Poor performance can result inunacceptable outcomes, such as loss of casualties, loss of life, andloss of assets.

SUMMARY OF THE DISCLOSURE

In an aspect, an exemplary system for determining actor status accordingto behavioral phenomena includes at least an eye sensor configured todetect at least an eye parameter as a function of at least an eyephenomenon, at least a speech sensor configured to detect at least aspeech parameter as a function of at least a speech phenomenon, and aprocessor in communication with the at least an eye sensor and the atleast a speech sensor; the processor is configured to receive the atleast an eye parameter and the at least a speech parameter, determine atleast an eye pattern as a function of the at least an eye parameter,determine at least a speech pattern as a function of the at least aspeech parameter, and correlate one or more of the at least an eyepattern and the at least a speech pattern to a cognitive status.

In another aspect, an exemplary method of determining actor statusaccording to behavioral phenomena may include detecting, using at leastan eye sensor, at least an eye parameter as a function of at least aneye phenomenon, detecting, using at least a speech sensor, at least aspeech parameter as a function of at least a speech phenomenon,receiving, using a processor in communication with the at least an eyesensor and the at least a speech sensor, the at least an eye parameterand the at least a speech parameter, determining, using the processor,at least an eye pattern as a function of the at least an eye parameter,determining, using the processor, at least a speech pattern as afunction of the at least a speech parameter, and correlating, using theprocessor, one or more of the at least an eye pattern and the at least aspeech pattern to a cognitive status.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram illustrating an exemplary system fordetermining actor status according to behavioral phenomena;

FIG. 2 illustrates an exemplary electromyography sensor;

FIG. 3A shows a perspective view of a device according to an embodimentdisclosed herein;

FIG. 3B shows a front view of a device according to an embodimentdisclosed herein;

FIG. 3C shows a side view of a device according to an embodimentdisclosed herein;

FIG. 3D shows a perspective view of a device according to an embodimentdisclosed herein;

FIG. 3E shows a front sectional view of a device according to anembodiment disclosed herein;

FIG. 4 illustrates an exemplary facemask;

FIG. 5 is a block diagram illustrating exemplary machine learningprocesses;

FIG. 6 is a block diagram illustrating an exemplary nodal network;

FIG. 7 is a block diagram illustrating an exemplary node;

FIG. 8 is a graph illustrating exemplary fuzzy set mathematics;

FIG. 9 is a flow diagram illustrating an exemplary method of determiningactor status according to behavioral phenomena;

FIG. 10 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed tosystems and methods for determining actor status according to behavioralphenomenon. In an embodiment, an actor may include a pilot, such as afighter pilot.

Aspects of the present disclosure can be used to ascertain a level ofcognitive ability of a person, as a function of objective phenomenaassociated with their behavior. Aspects of the present disclosure canalso be used to track cognitive ability over time for one or moreactors. This is so, at least in part, because an actor's cognitivestatus is likely change, for instance according to a number ofvariables, including without limitation levels of rest, levels ofhunger, and the like.

Aspects of the present disclosure allow for F. Exemplary embodimentsillustrating aspects of the present disclosure are described below inthe context of several specific examples.

Referring now to FIG. 1 , an exemplary embodiment of a system 100 fordetermining a status according to behavioral phenomenon is illustrated.In some cases, status may include an actor status. An “actor,” as usedin this disclosure, is one who is, at any point in time, engaged in anaction and/or responsibility of relative importance. An exemplary actorincludes a pilot, who is presently or may in the future be engaged in animportant task of flying an airplane. In some cases, status may includea pilot status. As used in this disclosure, “pilot status” is abehavioral or cognitive status of a pilot. As used in this disclosure,“behavioral phenomena” are observable physiological phenomena associatedwith conscious or unconscious actions of a person. Exemplarynon-limiting behavioral phenomenon include eye phenomenon, asrepresented by eye parameters and eye patterns, speech phenomenon, asrepresented by speech parameters and speech patterns, and the like.System 100 includes a processor 104. Processor 104 may include anycomputing device as described in this disclosure, including withoutlimitation a microcontroller, microprocessor, digital signal processor(DSP) and/or system on a chip (SoC) as described in this disclosure.Computing device may include, be included in, and/or communicate with amobile device such as a mobile telephone or smartphone. Processor 104may include a single computing device operating independently, or mayinclude two or more computing device operating in concert, in parallel,sequentially or the like; two or more computing devices may be includedtogether in a single computing device or in two or more computingdevices. Processor may interface or communicate with one or moreadditional devices as described below in further detail via a networkinterface device. Network interface device may be utilized forconnecting processor 104 to one or more of a variety of networks, andone or more devices. Examples of a network interface device include, butare not limited to, a network interface card (e.g., a mobile networkinterface card, a LAN card), a modem, and any combination thereof.Examples of a network include, but are not limited to, a wide areanetwork (e.g., the Internet, an enterprise network), a local areanetwork (e.g., a network associated with an office, a building, a campusor other relatively small geographic space), a telephone network, a datanetwork associated with a telephone/voice provider (e.g., a mobilecommunications provider data and/or voice network), a direct connectionbetween two computing devices, and any combinations thereof. A networkmay employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, softwareetc.) may be communicated to and/or from a computer and/or a computingdevice. Processor 104 may include but is not limited to, for example, acomputing device or cluster of computing devices in a first location anda second computing device or cluster of computing devices in a secondlocation. Processor 104 may include one or more computing devicesdedicated to data storage, security, distribution of traffic for loadbalancing, and the like. Processor 104 may distribute one or morecomputing tasks as described below across a plurality of computingdevices of computing device, which may operate in parallel, in series,redundantly, or in any other manner used for distribution of tasks ormemory between computing devices. Processor 104 may be implemented usinga “shared nothing” architecture in which data is cached at the worker,in an embodiment, this may enable scalability of system 100 and/orcomputing device.

With continued reference to FIG. 1 , processor 104 may be designedand/or configured to perform any method, method step, or sequence ofmethod steps in any embodiment described in this disclosure, in anyorder and with any degree of repetition. For instance, processor 104 maybe configured to perform a single step or sequence repeatedly until adesired or commanded outcome is achieved; repetition of a step or asequence of steps may be performed iteratively and/or recursively usingoutputs of previous repetitions as inputs to subsequent repetitions,aggregating inputs and/or outputs of repetitions to produce an aggregateresult, reduction or decrement of one or more variables such as globalvariables, and/or division of a larger processing task into a set ofiteratively addressed smaller processing tasks. Processor 104 mayperform any step or sequence of steps as described in this disclosure inparallel, such as simultaneously and/or substantially simultaneouslyperforming a step two or more times using two or more parallel threads,processor cores, or the like; division of tasks between parallel threadsand/or processes may be performed according to any protocol suitable fordivision of tasks between iterations. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which steps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

With continued reference to FIG. 1 , system 100 may determine a statusof a person 108. In some cases, person 108 may be an operator ofequipment. In some cases, person's 108 task may be physically and/orcognitively challenging as well as “high stakes.” For instance, in somecases consequences of a person's under-performance may be very high andresult in substantial loss of equipment and/or life. An exemplary suchperson 108 is a pilot 108, for example a fighter pilot 108.

With continued reference to FIG. 1 , system 100 may include at least aneye sensor 112. As used in this disclosure, an “eye sensor” is anysystem or device that is configured or adapted to detect an eyeparameter as a function of an eye phenomenon. In some cases, at least aneye sensor 112 may be configured to detect at least an eye parameter 116as a function of at least an eye phenomenon. As used in this disclosure,an “eye parameter” is an element of information associated with an eye.Exemplary non-limiting eye parameters may include blink rate,eye-tracking parameters, pupil location, gaze directions, pupildilation, and the like. Exemplary eye parameters are described ingreater detail below. In some cases, an eye parameter 116 may betransmitted or represented by an eye signal. An eye signal may includeany signal described in this disclosure. As used in this disclosure, an“eye phenomenon” may include any observable phenomenon associated withan eye, including without limitation focusing, blinking, eye-movement,and the like. Eye sensor 112 may include any sensor described in thisdisclosure, including with reference to FIG. 2 below. In someembodiments, at least an eye sensor 112 may include an electromyographysensor 112. Electromyography sensor may be configured to detect at leastan eye parameter 116 as a function of at least an eye phenomenon.

Still referring to FIG. 1 , in some embodiments, eye sensor 112 mayinclude an optical eye sensor 112. Optical eye sensor 112 may beconfigured to detect at least an eye parameter 116 as a function of atleast an eye phenomenon. In some cases, an optical eye sensor 112 mayinclude a camera directed toward one or both of person's 108 eyes. Insome cases, optical eye sensor 112 may include a light source, likewisedirected to person's 108 eyes. Light source may have a non-visiblewavelength, for instance infrared or near-infrared. In some cases, awavelength may be selected which reflects at an eye's pupil (e.g.,infrared). Light that selectively reflects at an eye's pupil may bedetected, for instance by camera. Images 116 of eyes may be captured bycamera 112. As used in this disclosure, a “camera” is a device that isconfigured to sense electromagnetic radiation, such as withoutlimitation visible light, and generate an image representing theelectromagnetic radiation. In some cases, a camera may include one ormore optics. Exemplary non-limiting optics include spherical lenses,aspherical lenses, reflectors, polarizers, filters, windows, aperturestops, and the like. In some cases, at least a camera may include animage sensor. Exemplary non-limiting image sensors include digital imagesensors, such as without limitation charge-coupled device (CCD) sensorsand complimentary metal-oxide-semiconductor (CMOS) sensors, chemicalimage sensors, and analog image sensors, such as without limitationfilm. In some cases, a camera may be sensitive within a non-visiblerange of electromagnetic radiation, such as without limitation infrared.As used in this disclosure, “image data” is information representing atleast a physical scene, space, and/or object (e.g., person 108 orperson's eyes). In some cases, image data 116 may be generated by acamera. “Image data” may be used interchangeably through this disclosurewith “image,” where image 116 is used as a noun. An image 116 may beoptical, such as without limitation where at least an optic is used togenerate an image 116 of an object 108. An image 116 may be material,such as without limitation when film is used to capture an image 116. Animage 116 may be digital, such as without limitation when represented asa bitmap. Alternatively, an image 116 may be comprised of any mediacapable of representing a physical scene, space, and/or object 108.Alternatively where “image” is used as a verb, in this disclosure, itrefers to generation and/or formation of an image 116.

Still referring to FIG. 1 , an exemplary camera 112 is an OpenMV Cam H7from OpenMV, LLC of Atlanta, Ga., U.S.A. OpenMV Cam includes a small,low power, microcontroller 104 which allows execution of processes.OpenMV Cam comprises an ARM Cortex M7 processor 104 and a 640×480 imagesensor 112 operating at a frame rate up to 150 fps. OpenMV Cam may beprogrammed with Python using a Remote Python/Procedure Call (RPC)library. OpenMV CAM may be used to operate image classification andsegmentation models, such as without limitation by way of TensorFlowLite; detect motion, for example by way of frame differencingalgorithms; detect markers, for example blob detection; detect objects,for example face detection; track eyes; detection persons, for exampleby way of a trained machine learning model; detect camera motion, forexample by way of optical flow detection; detect and decode barcodes;capture images; and record video.

Still referring to FIG. 1 , in some cases, a camera 112 may be used todetermine eye patterns 128 (e.g., track eye movements). For instance,camera 112 may capture images 116 and processor 104 (internal orexternal) to camera may process images 116 to track eye movements 128.In some embodiments, a video-based eye tracker may use cornealreflection (e.g., first Purkinje image) and a center of pupil asfeatures to track over time. A more sensitive type of eye-tracker, adual-Purkinje eye tracker, may use reflections from a front of cornea(i.e., first Purkinje image) and back of lens (i.e., fourth Purkinjeimage) as features to track. A still more sensitive method of trackingmay include use of image features from inside eye, such as retinal bloodvessels, and follow these features as the eye rotates. In some cases,optical methods, particularly those based on video recording, may beused for gaze-tracking and may be non-invasive and inexpensive.

For instance, in some cases a relative position between camera 112 andperson 108 may be known or estimable. Pupil location may be determinedthrough analysis of images (either visible or infrared images). In somecases, camera 112 may focus on one or both eyes and record eye movementas viewer 108 looks. In some cases, eye-tracker 112 may use center ofpupil and infrared/near-infrared non-collimated light to create cornealreflections (CR). A vector between pupil center and corneal reflectionscan be used to compute a point of regard on surface (i.e., a gazedirection). In some cases, a simple calibration procedure with anindividual person 108 may be needed before using an optical eye tracker112. In some cases, two general types of infrared/near-infrared (alsoknown as active light) eye-tracking techniques can be used: bright-pupil(light reflected by pupil) and dark-pupil (light not reflected bypupil). Difference between bright-pupil and dark pupil images 116 may bebased on a location of illumination source with respect to optics. Forinstance, if illumination is coaxial with optical path, then eye may actas a retroreflector as the light reflects off retina creating a brightpupil effect similar to red eye. If illumination source is offset fromoptical path, then pupil may appear dark because reflection from retinais directed away from camera 112. In some cases, bright-pupil trackingcreates greater iris/pupil contrast, allowing more robust eye-trackingwith all iris pigmentation, and greatly reduces interference caused byeyelashes and other obscuring features. In some cases, bright-pupiltracking may also allow tracking in lighting conditions ranging fromtotal darkness to very bright.

Still referring to FIG. 1 , alternatively, in some cases, a passivelight optical eye tracking method may be employed. Passive light opticaleye tracking may use visible light to illuminate. In some cases, passivelight optical tracking yields less contrast of pupil than with activelight methods; therefore, in some cases, a center of iris may be usedfor calculating a gaze vector. In some cases, a center of irisdetermination requires detection of a boundary of iris and sclera (e.g.,limbus tracking). In some case, eyelid obstruction of iris and oursclera may challenge calculations of an iris center.

Still referring to FIG. 1 , some optical eye tracking systems may behead-mounted, some may require the head to be stable, and some mayfunction remotely and automatically track the head during motion.Optical eye tracking systems 112 may capture images 116 at frame rate.Exemplary frame rates include 15, 30, 60, 120, 240, 350, 1000, and 1250Hz.

With continued reference to FIG. 1 , system may include at least aspeech sensor 120. As used in this disclosure, a “speech sensor” is anysystem or device that is configured or adapted to detect a speechparameter as a function of a speech phenomenon. In some cases, speechsensor 120 may be configured to detect at least a speech parameter 124as a function of at least a speech phenomenon. As used in thisdisclosure, a “speech parameter” is an element of information associatedwith speech. An exemplary non-limiting speech parameter is arepresentation of at least a portion of audible speech, for instance adigital representation of audible speech. In some cases, a speechparameter 124 may be transmitted or represented by a speech signal. Aspeech signal may include any signal described in this disclosure. Asused in this disclosure, a “speech phenomenon” may include anyobservable phenomenon associated with speech, including withoutlimitation audible phenomena and/or acoustic phenomena. Speech phenomenamay include pressure changes, for instance audible pressure changes asdetectable by a microphone. In some cases, speech phenomenon may not bedirectly related to speech, and may include phenomena related tobreathing. For example, breathing sounds may be detected by speechsensor 120 and used as speech parameter 124. Speech sensor 120 mayinclude any sensor described in this disclosure, including withreference to FIGS. 3A-3E below. In some embodiments, at least a speechsensor 120 may include a bone conductance transducer 120. In some cases,bone conductance transducer 120 may be configured to detect at least aspeech parameter 124 as a function of at least a speech phenomenon, seeFIGS. 3A-3E. In some cases, system 100 may utilize communication signalsand use them as representation of speech parameters 124. For instance,in some cases, a person 108 may already be in audible communication withothers, through communication microphones. These communication signalsmay be used by system 100 as speech parameters 124.

Still referring to FIG. 1 , in some cases, speech sensor 120 may includea microphone, for example an air spaced microphone. In some cases,microphone 120 may be configured to detect at least a speech parameter124 as a function of at least a speech phenomenon. In some cases, speechphenomenon may include sound or sounds associated with speech, i.e.,oral verbalization. As used in this disclosure, a “microphone” is anytransducer configured to transduce pressure change phenomenon to asignal, for instance a signal representative of a parameter associatedwith the phenomenon. Microphone, according to some embodiments, mayinclude a transducer configured to convert sound into an audio signal.Exemplary non-limiting microphones include dynamic microphones (whichmay include a coil of wire suspended in a magnetic field), condensermicrophones (which may include a vibrating diaphragm condensing plate),and a contact (or conductance) microphone (which may includepiezoelectric crystal material). Microphone 120 may include anymicrophone for transducing pressure changes, as described above;therefore, microphone 120 may include any variety of microphone,including any of: condenser microphones, electret microphones, dynamicmicrophones, ribbon microphones, carbon microphones, piezoelectricmicrophones, fiber-optic microphones, laser microphones, liquidmicrophones, microelectromechanical systems (MEMS) microphones, and/or aspeaker microphone. An “audio signal,” as used in this disclosure, is arepresentation of sound. In some cases, an audio signal may include ananalog electrical signal of time-varying electrical potential. In someembodiments, an audio signal may be communicated (e.g., transmittedand/or received) by way of an electrically transmissive path (e.g.,conductive wire), for instance an audio signal path. Alternatively oradditionally, audio signal may include a digital signal of time-varyingdigital numbers. In some cases, a digital audio signal may becommunicated (e.g., transmitted and/or received) by way of any of anoptical fiber, at least an electrically transmissive path, and the like.In some cases, a line code and/or a communication protocol may be usedto aid in communication of a digital audio signal. Exemplary digitalaudio transports include, without limitation, Alesis Digital Audio Tape(ADAT), Tascam Digital Interface (TDIF), Toshiba Link (TOSLINK),Sony/Philips Digital Interface (S/PDIF), Audio Engineering Societystandard 3 (AES3), Multichannel Audio Digital Interface (MADI), MusicalInstrument Digital Interface (MIDI), audio over Ethernet, and audio overIP. Audio signals may represent frequencies within an audible rangecorresponding to ordinary limits of human hearing, for examplesubstantially between about 20 and about 20,000 Hz. According to someembodiments, an audio signal may include one or more parameters, such aswithout limitation bandwidth, nominal level, power level (e.g., indecibels), and potential level (e.g., in volts). In some cases,relationship between power and potential for an audio signal may berelated to an impedance of a signal path of the audio signal. In somecases, a signal path may single-ended or balanced.

With continued reference to FIG. 1 , processor 104 may be incommunication with at least an eye sensor 112 and/or at least a speechsensor 116. Processor 104 may communicate with at least an eye sensor112 and/or at least a speech sensor 116 using any method, including byway of communication signals. As used in this disclosure, a “signal” isany intelligible representation of data, for example from one device toanother. A signal may include an optical signal, a hydraulic signal, apneumatic signal, a mechanical, signal, an electric signal, a digitalsignal, an analog signal and the like. In some cases, a signal may beused to communicate with a computing device (processor 104), for exampleby way of one or more ports. In some cases, a signal may be transmittedand/or received by a computing device (processor 104) for example by wayof an input/output port. An analog signal may be digitized, for exampleby way of an analog to digital converter. In some cases, an analogsignal may be processed, for example by way of any analog signalprocessing steps described in this disclosure, prior to digitization. Insome cases, a digital signal may be used to communicate between two ormore devices, including without limitation computing devices. In somecases, a digital signal may be communicated by way of one or morecommunication protocols, including without limitation internet protocol(IP), controller area network (CAN) protocols, serial communicationprotocols (e.g., universal asynchronous receiver-transmitter [UART]),parallel communication protocols (e.g., IEEE 128 [printer port]), andthe like.

Still referring to FIG. 1 , in some cases, system 100, for instance atleast an eye sensor 112, at least a speech sensor 120, and/or processor104 may perform one or more signal processing steps on a signal. Forinstance, system 100 may analyze, modify, and/or synthesize a signalrepresentative of data in order to improve the signal, for instance byimproving transmission, storage efficiency, or signal to noise ratio.Exemplary methods of signal processing may include analog, continuoustime, discrete, digital, nonlinear, and statistical. Analog signalprocessing may be performed on non-digitized or analog signals.Exemplary analog processes may include passive filters, active filters,additive mixers, integrators, delay lines, compandors, multipliers,voltage-controlled filters, voltage-controlled oscillators, andphase-locked loops. Continuous-time signal processing may be used, insome cases, to process signals which varying continuously within adomain, for instance time. Exemplary non-limiting continuous timeprocesses may include time domain processing, frequency domainprocessing (Fourier transform), and complex frequency domain processing.Discrete time signal processing may be used when a signal is samplednon-continuously or at discrete time intervals (i.e., quantized intime). Analog discrete-time signal processing may process a signal usingthe following exemplary circuits sample and hold circuits, analogtime-division multiplexers, analog delay lines and analog feedback shiftregisters. Digital signal processing may be used to process digitizeddiscrete-time sampled signals. Commonly, digital signal processing maybe performed by a computing device or other specialized digitalcircuits, such as without limitation an application specific integratedcircuit (ASIC), a field-programmable gate array (FPGA), or a specializeddigital signal processor (DSP). Digital signal processing may be used toperform any combination of typical arithmetical operations, includingfixed-point and floating-point, real-valued and complex-valued,multiplication and addition. Digital signal processing may additionallyoperate circular buffers and lookup tables. Further non-limitingexamples of algorithms that may be performed according to digital signalprocessing techniques include fast Fourier transform (FFT), finiteimpulse response (FIR) filter, infinite impulse response (IIR) filter,and adaptive filters such as the Wiener and Kalman filters. Statisticalsignal processing may be used to process a signal as a random function(i.e., a stochastic process), utilizing statistical properties. Forinstance, in some embodiments, a signal may be modeled with aprobability distribution indicating noise, which then may be used toreduce noise in a processed signal.

With continued reference to FIG. 1 , processor 104 may receive one ormore of at least an eye parameter 116 and at least a speech parameter124. In some cases, processor 104 determine at least an eye pattern 128as a function of the at least an eye parameter 116. As used in thisdisclosure, an “eye pattern” is a representation of an eye-relatedbehavioral phenomenon. In some cases, an eye pattern 128 may be derivedor otherwise determined from an eye parameter 116. In some cases, eyeparameters 116 (e.g., images from optical tracker or electrical signalsfrom electromyography sensor) may be used to ascertain eye movements116. Eye movements 116 may be divided into fixations and saccades.Fixations may occur when eye gaze pauses in a certain position. Saccadesmay occur when eye gaze moves to another position. A resulting series offixations and saccades may be called a scanpath 116. Smooth pursuitdescribes a scanpath of an eye following a moving object. Fixational eyemovements 116 include microsaccades: small, involuntary saccades thatoccur during attempted fixation. Most information from an eye is madeavailable to a viewer 108 during a fixation or smooth pursuit, but notduring a saccade. Scanpaths 116 may be useful for analyzing cognitiveintent, interest, and salience. Other biological factors may affect thescanpath 116 as well. In some cases, eye parameter may include 116include blink rate. As used in this disclosure, is any time relatedvariable associated with movement of an eyelid. Exemplary, blink ratesinclude number of blinks over a certain time, average frequency ofblinks, amount of time per blink, delay time between stimulation and ablink (e.g., corneal reflex), and the like.

Still referring to FIG. 1 , in some embodiments, processor 104 maydetermine at least an eye pattern 128 using one or more machine learningprocesses. Exemplary machine learning processes are described in detailwith reference to FIGS. 5-8 . For example, in some cases, processor 104may receive an eye pattern training data. As used in this disclosure,“eye pattern training data” is a training set that correlates eyeparameters to eye patterns. In some cases, eye pattern training data maybe compiled from historic information, for instance by a user. In somecases, eye pattern training data may be compiled by an unsupervisedmachine learning process. Eye pattern training data may use eyeparameters correlated to eye patterns for one individual user, or for acohort or population of users. Historic information may includeinformation from eye-related study. In some cases, historicalinformation may include information captured from use of system 100.Processor 104 may input eye pattern training data into an eye patternmachine learning algorithm. As used in this disclosure, an “eye patternmachine learning algorithm” is any machine learning algorithm that isconfigured to train an eye pattern machine learning model using eyepattern training data. Processor 104 may train an eye pattern machinelearning model 136, as a function of eye pattern machine learningalgorithm. As used in this disclosure, “eye pattern machine learningmodel” is a machine learning model that is configured to take as inputat least an eye parameter and output at least a correlated eye pattern.Processor 104 may determine at least an eye pattern 128 as a function ofeye pattern machine learning model 136 and at least an eye parameter116.

With continued reference to FIG. 1 , processor 104 may determine atleast a speech pattern 132 as a function of the at least a speechparameter 124. As used in this disclosure, a “speech pattern” is arepresentation of a speech-related behavioral phenomenon. In some cases,a speech pattern may be derived or otherwise determined from a speechparameter. Exemplary speech patterns include timber, pitch, and cadenceof speech. In some cases, speech pattern may be unrelated to content ofan actor's 108 speech. Instead, in some cases, speech pattern 132 may berelated to changes audible characteristics of actor's speech. In somecases, speech pattern 132 may be derived through analysis of speechparameters 124, for instance audio analysis described above. Speechpattern 132 may include one or more prosodic variables. As used in thisdisclosure, “prosodic variables” are variables that relate to spokensyllables or larger speech units. In some cases, speech pattern 132 mayinclude audible variables, for instance pitch, change in pitch, lengthof units of speech (e.g., syllables), volume, loudness, prominence(i.e., relative volume of a unit speech, timbre, quality of sound, andthe like. In some cases, speech pattern 132 may include acoustic terms.Acoustic terms may include without limitation fundamental frequency,duration, intensity, sound pressure, spectral characteristics, and thelike. Speech pattern 132 may include speech tempo. As used in thisdisclosure, “speech tempo” is a measure of a number of speech unitswithin a certain amount of time. Speech tempo may vary within speech ofone person, for instance according to context and emotional factors.Speech tempo may have units of syllables per second.

Still referring to FIG. 1 , in some embodiments, processor 104 may beconfigured to determine a speech pattern 132 by using one or moremachine learning processes. Exemplary machine learning processes aredescribed in detail with reference to FIGS. 5-8 . For example, in somecases, processor 104 may receive a speech pattern training data. As usedin this disclosure, “speech pattern training data” is a training setthat correlates speech parameters to speech patterns. In some cases,speech pattern training data may be compiled from historic information,for instance by a user. In some cases, speech pattern training data maybe compiled by an unsupervised machine learning process. Speech patterntraining data may use speech parameters correlated to speech patternsfor one individual user, or for a cohort or population of users.Historic information may include information from speech-related study.In some cases, historical information may include information capturedfrom use of system 100. Processor 104 may input speech pattern trainingdata into a speech pattern machine learning algorithm. As used in thisdisclosure, a “speech pattern machine learning algorithm” is any machinelearning algorithm that is configured to train a speech pattern machinelearning model using speech pattern training data. Processor 104 maytrain a speech pattern machine learning model 140, as a function ofspeech pattern machine learning algorithm. As used in this disclosure,“speech pattern machine learning model” is a machine learning model thatis configured to take as input at least a speech parameter and output atleast a correlated speech pattern. Processor 104 may determine at leasta speech pattern 132 as a function of speech pattern machine learningmodel 140 and at least a speech parameter 124.

With continued reference to FIG. 1 , processor 104 may correlate one ormore of at least an eye pattern 128 and at least a speech pattern 132 toa cognitive status 144 As used in this disclosure, “cognitive status” isa representation of mental performance. Exemplary cognitive statuses mayinclude classifications, for instance impaired or unimpaired.Alternatively or additionally, cognitive status may include a relativeor absolute continuously variable measure that indicates performance.For example, cognitive status may be represented as a proportion orpercentage relative an ideal or satisfactory cognitive performance.Cognitive performance can be pegged relative a user's performance.Alternatively or additionally, in some cases, cognitive performance maybe pegged relative a level of cognitive performance required for a giventask or responsibility.

Still referring to FIG. 1 , in some embodiments, processor 104 may beconfigured to determine a cognitive status 144 by using one or moremachine learning processes. Exemplary machine learning processes aredescribed in detail with reference to FIGS. 5-8 . In some cases,processor 104 may receive a cognitive status training data. As used inthis disclosure, “cognitive status training data” is a training set thatcorrelates behavioral parameters, such as without limitation eyepatterns, eye parameters, speech patterns, and speech parameters tocognitive statuses. In some cases, cognitive status training data may becompiled and/or correlated from historic information, for instance by auser. In some cases, cognitive status training data may be compiledand/or correlated by an unsupervised machine learning process. Cognitivestatus training data may use behavioral parameters correlated tocognitive status for one individual user, or for a cohort or populationof users. Historic information may include information from cognitivestatus-related study. In some cases, historical information may includeinformation captured from use of system 100. Processor 104 may inputcognitive status training data into a cognitive status machine learningalgorithm. As used in this disclosure, an “cognitive status machinelearning algorithm” is any machine learning algorithm that is configuredto train a cognitive status machine learning model using cognitivestatus training data. Processor 104 may train a cognitive status machinelearning model 148, as a function of the cognitive status machinelearning algorithm. As used in this disclosure, “cognitive statusmachine learning model” is a machine learning model that is configuredto take as input at least a behavioral parameter, such as withoutlimitation eye patterns, eye parameters, speech patterns, and speechparameters, and output at least a correlated cognitive status. Processor104 may determine cognitive status 144 as a function of cognitive statusmachine learning model 148 and one or more of at least an eye pattern128 and the least a speech pattern 132.

Still referring to FIG. 1 , in some cases one or more of eye machinelearning model 136, speech machine learning model 140, and cognitivestatus machine learning model 148 may include a classifier, such as anyclassifier described in detail below. In some cases, classifier mayinclude calculation of distance. For instance classifier may employfuzzy sets having coverages based on distances from differentcentroids/neighbors (e.g., probability of set membership based on degreeof closeness to a corresponding classification). In some cases,classifier may include a fuzzy inference engine configured to determineone or more classification set membership probability determinations.Inference engine may include a sum-product, max-product, min-max engine,or the like. In some cases, classifier may perform one or more rulesand/or actions as a function of output from fuzzy inference engine,thereby dealing with different overlapping fuzzy sets (i.e.,classifications). Fuzzy set classification is described in greaterdetail with reference to FIG. 8 below.

Still referring to FIG. 1 , in some embodiments, processor 104 may beadditionally configured to correlate one or more of at least an eyeparameter 116 and at least a speech parameter 124 to the cognitivestatus 144. For instance, in some cases, processor 104 may make adetermination of cognitive status 144 without an intervening step ofdetermining a speech pattern 132 and/or an eye pattern 128.

Still referring to FIG. 1 , in some embodiments, processor 104 may beadditionally configured to determine a confidence metric associated withcorrelation and/or determination of cognitive status 144. As used inthis disclosure, a “confidence metric” is a quantified expression ofconfidence associated with a function, such as a likelihood orprobability that an output of a function is accurate or correct.Determination of a confidence metric may include any appropriate processdescribed in this disclosure, including for example with reference toFIGS. 5-8 . Exemplary processes for determining a confidence metricinclude, without limitation, fuzzy mathematics (see FIG. 8 ), as well ascalculation of a distance metric (see FIG. 5 ). In some cases, aconfidence metric may be a proportional or unitless figure, for exampleexpressed in terms of a proportion or percentage. Alternatively ofadditionally, a confidence metric may be represented using relative orabsolute units. In some cases, a confidence metric may be compared to athreshold confidence metric in order to determine suitability of anassociated correlation and/or determination, for example of a cognitivestatus 144. For instance, in some cases a confidence metric no less thana threshold confidence metric of 95%, 90%, 85%, 75%, or 50% is requiredin order to assure an underlying correlation and/or determination ofcognitive status is “correct.”

Referring now to FIG. 2 an exemplary electromyography (EMG) sensor 200is illustrated. In some cases, electromyography (EMG) may be anelectrodiagnostic medicine technique for evaluating and recordingelectrical activity produced by skeletal muscles. EMG may be performedusing an instrument called an electromyograph to produce a record calledan electromyogram. An electromyograph may detect electric potentialgenerated by muscle cells, for instance when these cells areelectrically or neurologically activated. Resulting electromyographicsignals can be analyzed to detect medical abnormalities, activationlevel, or recruitment order, or to analyze the biomechanics of human oranimal movement. In some cases, EMG may also be used as middleware ingesture recognition towards allowing input of physical action to acomputing device or as a form of human-computer interaction. In somecases, an EMG sensor 200 may be located about an eye of a user and usedto detect eye movements and/or blinks, for instance through detection ofelectrical activity of extraocular muscles. An EMG sensor 200 mayinclude at least a ground electrode 204 and at least an EMG electrode208. In some cases, a ground electrode 204 may be placed substantiallyaway from an eye and/or extraocular muscles. In some cases, a groundelectrode 204 may be electrically isolated (i.e., floating), therebyallowing detection of muscular electrical activity relative the bodyrather than relative a ground or other reference. In some cases, EMGsignals may be substantially made up of superimposed motor unit actionpotentials (MUAPs) from several motor units (e.g., muscles). EMG signalscan be decomposed into their constituent MUAPs. MUAPs from differentmotor units tend to have different characteristic shapes, while MUAPsrecorded by the same electrode from the same motor unit are typicallysimilar. Notably MUAP size and shape depend on where the electrode islocated with respect to muscle fibers and so can appear different if anelectrode 204, 208 moves position. EMG decomposition may involve anysignal processing methods described in this disclosure, including thosebelow.

With continued reference to FIG. 2 , in some case EMG signalrectification may include translation of a raw EMG signal to a signalwith a single polarity, for instance positive. In some cases, rectifyingan EMG signal may be performed to ensure the EMG signal does not averageto zero, as commonly a raw EMG signal may have positive and negativecomponents. According to some embodiments, substantially two types ofEMG signal rectification may be used full-wave and half-waverectification. As used in this disclosure, “full-wave rectification” mayadd EMG signal below a baseline to the EMG signal above the baseline,thereby resulting in a conditioned EMG signal that is all positive. Forexample, if baseline of EMG signal is zero, full-wave rectificationwould be equivalent to taking an absolute value of the EMG signal.According to some embodiments, full-wave rectification may conservesubstantially all of EMG signal energy for analysis. As used in thisdisclosure, “half-wave rectification” discards a portion of EMG signalbelow baseline. As a result of half-wave rectification, average of EMGsignal may no longer be zero; therefore, an EMG signal conditioned byhalf-wave rectification can be used in further statistical analyses. Insome cases, EMG signal measurement may be performed at a rate no lessthan 1 Hz, 10 Hz, 100 Hz, 1 Khz, 10 KHz, or 100 KHz.

Still referring to FIG. 2 , in some embodiments, EMG sensor 200 maydetect eye parameters that can be analyzed, for example using processor104, by way of time of arrival or time difference of arrival methods. Asused in this disclosure, “time of arrival” is an absolute time when asignal reaches a receiver. In some cases, a plurality of EMG electrodes308 may be located at different locations proximal an eye. Difference intime of arrival of signals between EMG electrodes 308 can be indicativeof physiological phenomena. In some cases, propagation velocity may bedetermined. Propagation velocity may be a measure of distance over time,for instance distance a signal has traveled over time for signal totravel. In some cases, propagation velocity of EMG signals may be usedto determine eye patterns. For instance, in some cases, blink rate maybe ascertained from propagation velocity and/or other time of arrivalmetrics.

Still referring to FIG. 2 , in some embodiments, EMG sensor 200 may beused to detect a gaze of user and/or the gaze of the user over time. Asused in this disclosure, “gaze” is a direction a user is looking. Asused in this disclosure “gaze vector” is a directional vector having apoint located at a user's eye (e.g., pupil, retina, or the like) whichrepresents a gaze of the user. In some cases, an EMG sensor 200 may beused to detect a gaze of a user over time and this information may beused as input for one or more machine-learning models described herein.In some cases, eye parameters from EMG sensor 200 may be used as inputfor an eye pattern machine learning process that used to determine aneye pattern (e.g., gaze vector, scan path, blink rate, or the like).Alternatively or additionally, in some cases, eye parameters and/or eyepatterns, such as a user's blink rate as detected by EMG sensor 200, maybe used as an input for a cognitive status machine learning process.This is because, for example, users who blink more frequently may beless attentive (e.g., drowsier) than those who blink less. For example,in an extreme case a user whose eyes are closed for prolonged periods oftime may be found to be inattentive, perhaps even asleep.

In an embodiment, systems, devices and methods disclosed herein detectphysiological parameters such as blood oxygen level, blood pressure,neurological oscillations, and heart rate of a user who is operating anitem of equipment such as an aircraft through nonintrusive means.Sensors mounted in optimal locations on the head or neck of the user maydetect physiological parameters accurately, minimizing interference inactivities the user engages in while obtaining a clearer signal thanotherwise would be possible. Embodiments of the disclosed device mayprovide users such as pilots, firemen, and divers who are operatingunder extreme circumstances with an early warning regarding potentialcrises such as loss of consciousness, affording the user a few preciousextra seconds to avert disaster. Alarms may be provided to the user viabone-conducting transducers or by integration into displays the user isoperating, increasing the likelihood that the user will notice thewarning in time. Embodiments of devices, systems, and methods herein mayenable training for pilots or other persons to function withinphysiological limitations imposed by their environment, such ashypoxemia imposed by altitude, high G forces and the like; training mayfurther enable users to learn how to avoid total impairment, and tofunction under partial impairment.

Referring now to FIGS. 3A-3E, an exemplary embodiment of a perspectiveview (FIG. 3A), a side view (FIG. 3B), a front view (FIG. 3C), aperspective view (FIG. 3D), and a front sectional view (FIG. 3E) of adevice for measuring physiological parameters 300 is illustrated.Referring now to FIG. 3A, device for measuring physiological parameters300 includes a housing 304. Housing 304 may be mounted to an exteriorbody surface of a user; exterior body surface may include, withoutlimitation, skin, nails such as fingernails or toenails, hair, aninterior surface of an orifice such as the mouth, nose, or ears, or thelike. A locus on exterior body surface for mounting of housing 304and/or other components of device may be selected for particularpurposes as described in further detail below. Exterior body surfaceand/or locus may include an exterior body surface of user's head, face,or neck. Housing 304 may be constructed of any material or combinationof materials, including without limitation metals, polymer materialssuch as plastics, wood, fiberglass, carbon fiber, or the like. Housing304 may include an outer shell 308. Outer shell 308 may, for instance,protect elements of device 300 from damage, and maintain them in acorrect position on a user's body as described in further detail below.Housing 304 and/or outer shell 308 may be shaped, formed, or configuredto be inserted between a helmet worn on a head of the user and theexterior body surface; housing 304 and/or outer shell 308 may be shapedto fit between the helmet and the exterior body surface. As anon-limiting example, exterior body surface may be a surface, such as asurface of the head, face, or neck of user, which is wholly or partiallycovered by helmet, as described for example in further detail below. Asa further non-limiting example, housing 304 may be formed to have asimilar or identical shape to a standard-issue “ear cup” incorporated inan aviation helmet, so that housing 304 can replace ear cup after earcup has been removed; in an embodiment, device 300 may incorporate oneor more elements of ear-cup, including sound-dampening properties, oneor more speakers or other elements typically used to emit audio signalsin headsets or headphones, or the like. As a non-limiting example,device 300, housing 304, and/or shell may form a form-fit replacementfor standard earcups found in military flight helmets. Shell may berigid, where “rigid” is understood as having properties of an exteriorcasing typically used in an earcup, over-ear headphone, hearingprotection ear covering, or the like; materials used for such a shellmay include, without limitation, rigid plastics such as polycarbonateshell plastics typically used in helmets and hardhats, metals such assteel, and the like. Persons skilled in the art, upon reading theentirety of this disclosure, will understand “rigid” in this context assignifying sufficient resistance to shear forces, deformations, andimpacts to protect electronic components as generally required fordevices of this nature.

Still viewing FIGS. 3A-3E, housing 304 may include a seal 312 that restsagainst exterior body surface when housing 304 is mounted thereon. Seal312 may be pliable; seal 312 may be constructed of elastomeric, elastic,or flexible materials including without limitation flexible,elastomeric, or elastic rubber, plastic, silicone including medicalgrade silicone, gel, and the like. Pliable seal 312 may include anycombination of materials demonstrating flexible, elastomeric, or elasticproperties, including without limitation foams covered with flexiblemembranes or sheets of polymer, leather, or textile material. As anon-limiting example, pliable seal 312 may include any suitable pliablematerial for a skin-contacting seal portion of an earcup or other deviceconfigured for placement over a user's ear, including without limitationany pliable material or combination of materials suitable for use onheadphones, headsets, earbuds, or the like. In an embodiment, pliableseal 312 advantageously aids in maintaining housing 304 and/or othercomponents of device 300 against exterior body surface; for instance,where exterior body surface has elastomeric properties and may beexpected to flex, stretch, or otherwise alter its shape or position toduring operation, pliable seal 312 may also stretch, flex, or otherwisealter its shape similarly under similar conditions, which may have theeffect of maintaining seal 312 and/or one or more components of device300 as described in greater detail below, in consistent contact with theexterior body surface. Seal 312 may be attached to housing 304 by anysuitable means, including without limitation adhesion, fastening bystitching, stapling, or other penetrative means, snapping together orotherwise engaging interlocking parts, or the like. Seal 312 may beremovably attached to housing 304, where removable attachment signifiesattachment according to a process that permits repeated attachment anddetachment without noticeable damage to housing 304 and/or seal 312, andwithout noticeable impairment of an ability to reattach again by thesame process. As a non-limiting example, pliable seal 312 may be placedon an ear cup (for instance shown for exemplary purposes in FIG. 3 ) ofthe housing 304; pliable seal maybe formed of materials and/or in ashape suitable for use as an ear seal in an ear cup of a helmet, anover-ear headphone or hearing protection device, or the like. Personsskilled in the art, upon reviewing this disclosure in its entirety, willbe aware of forms and material properties suitable for use as seal 312,including without limitation a degree and/or standard of pliabilityrequired and/or useful to function as a seal 312 in this context.

With continued reference to FIGS. 3A-3E, housing 304 may include, beincorporated in, or be attached to an element containing additionalcomponents to device 300. For instance, in an embodiment, housing 304may include, be incorporated in, or be attached to a headset; headsetmay include, without limitation, an aviation headset, such as headsetsas manufactured by the David Clark company of Worcester Mass., orsimilar apparatuses. In some embodiments, housing 104 is headset; thatis, device 300 may be manufactured by incorporating one or morecomponents into the headset, using the headset as a housing 304. As afurther non-limiting example, housing 304 may include a mask; a mask asused herein may include any device or element of clothing that is wornon a face of user during operation, occluding at least a part of theface. Masks may include, without limitation, safety googles, gas masks,dust masks, self-contained breathing apparatuses (SCBA), self-containedunderwater breathing apparatuses (SCUBA), and/or other devices worn onand at least partially occluding the face for safety, functional, oraesthetic purposes. Housing 304 may be mask; that is, device 300 may bemanufactured by incorporating one or more elements or components ofdevice 300 in or on mask, using mask as housing 304. Housing 304 mayinclude, be incorporated in, or be attached to an element of headgear,defined as any element worn on and partially occluding a head or craniumof user. Headgear may wholly or partially occlude user's face and thusalso include a mask; headgear may include, for instance, a fullyenclosed diving helmet, space helmet or helmet incorporated in a spacesuit, or the like. Headgear may include a headband, such as withoutlimitation a headband of a headset, which may be an aviation headset.Headgear may include a hat. Headgear may include a helmet, including amotorcycle helmet, a helmet used in automobile racing, any helmet usedin any military process or operation, a construction “hardhat,” abicycle helmet, or the like. In an embodiment, housing 304 is shaped toconform to a particular portion of user anatomy when placed on exteriorbody surface; when placed to so conform, housing 304 may position atleast a sensor and/or user-signaling device 328 in a locus chosen asdescribed in further detail below. For instance, where housing 304 isincorporated in a helmet, mask, earcup or headset, housing 304 may bepositioned at a particular portion of user's head when helmet, mask,earcup or headset is worn, which may in turn position at least a sensorand/or user-signaling device 328 at a particular locus on user's head orneck.

Continuing to refer to FIGS. 3A-3E, device 300 includes at least aphysiological sensor 316. At least a physiological sensor 316 isconfigured to detect at least a physiological parameter and transmit anelectrical signal as a result of the detection; transmission of anelectrical signal, as used herein, includes any detectable alternationof an electrical parameter of an electrical circuit incorporating atleast a physiological sensor 316. For instance, at least a physiologicalsensor 316 may increase or reduce the impedance and/or resistance of acircuit to which at least a physiological sensor 316 is connected. Atleast a physiological sensor 316 may alter a voltage or current level,frequency, waveform, amplitude, or other characteristic at a locus incircuit. Transmission of an electrical signal may include modulation oralteration of power circulating in circuit; for instance transmissionmay include closing a circuit, transmitting a voltage pulse throughcircuit, or the like. Transmission may include driving a non-electricsignaling apparatus such as a device for transmitting a signal usingmagnetic or electric fields, electromagnetic radiation, optical orinfrared signals, or the like.

Still referring to FIGS. 3A-3E, at least a physiological parameter mayinclude any datum that may be captured by a sensor and describing aphysiological state of user. At least a physiological parameter mayinclude at least a circulatory and/or hematological parameter, which mayinclude any detectable parameter describing the state of blood vesselssuch as arteries, veins, or capillaries, any datum describing the rate,volume, pressure, pulse rate, or other state of flow of blood or otherfluid through such blood vessels, chemical state of such blood or otherfluid, or any other parameter relative to health or currentphysiological state of user as it pertains to the cardiovascular system.As a non-limiting example, at least a circulatory parameter may includea blood oxygenation level of user's blood. At least a circulatoryparameter may include a pulse rate. At least a circulatory parameter mayinclude a blood pressure level. At least a circulatory parameter mayinclude heart rate variability and rhythm. At least a circulatoryparameter may include a plethysmograph describing user blood-flow; in anembodiment, plethysmograph may describe a reflectance of red ornear-infrared light from blood. One circulatory parameter may be used todetermine, detect, or generate another circulatory parameter; forinstance, a plethysmograph may be used to determine pulse and/or bloodoxygen level (for instance by detecting plethysmograph amplitude), pulserate (for instance by detecting plethysmograph frequency), heart ratevariability and rhythm (for instance by tracking pulse rate and otherfactors over time), and blood pressure, among other things. At least aphysiological sensor may be configured to detect at least ahematological parameter of at least a branch of a carotid artery; atleast a physiological parameter may be positioned to capture the atleast a hematological parameter by placement on a location of housingthat causes at least a physiological sensor to be placed in closeproximity to the at least a branch; for instance, where housing isconfigured to be mounted to a certain location on a user's cranium, andin a certain orientation, such as when housing forms all or part of ahelmet, headset, mask, element of headgear, or the like, at least aphysiological sensor may include a sensor so positioned on the housingor an extension thereof that it will contact or be proximate to a locuson the user's skin under which the at least a branch runs. As anon-limiting example, where device 300 forms an earcup or earphone, atleast a physiological sensor 316 may include a sensor disposed on orembedded in a portion of the earcup and/or earphone contacting a user'sskin over a major branch of the external carotid artery that runs nearor past the user's ear.

In an embodiment, and still viewing FIGS. 3A-3E, detection ofhematological parameters of at least a branch of a carotid artery mayenable device 300 to determine hematological parameters of a user'scentral nervous system with greater accuracy than is typically found indevices configured to measure hematological parameters. For instance, ablood oxygen sensor placed on a finger or other extremity may detect lowblood oxygen levels in situations in which the central nervous system isstill receiving adequate oxygen, because a body's parasympatheticresponse to decreasing oxygen levels may include processes whereby bloodperfusion to the appendages is constricted in order to sustain higheroxygen levels to the brain; in contrast, by directly monitoring theoxygenation of a major branch of the external carotid artery, themeasurement of oxygenation to the central nervous system may be morelikely to achieve a more accurate indication of oxygen saturation than aperipheral monitor. Use of the carotid artery in this way may furtherresult in a more rapid detection of a genuine onset of hypoxemia; as aresult, a person such as a pilot that is using device 300 may be able tofunction longer under conditions tending to induce hypoxemia, knowingthat an accurate detection of symptoms may be performed rapidly andaccurately enough to warn the user. This advantage may both aid in andbe augmented by use with training processes as set forth in furtherdetail below.

With continued reference to FIGS. 3A-3E, at least a physiological sensor316 may include a hydration sensor; hydration sensor may determine adegree to which a user has an adequate amount of hydration, wherehydration is defined as the amount of water and/or concentration ofwater versus solutes such as electrolytes in water, in a person's body.Hydration sensor may use one or more elements of physiological data,such as sweat content and/or hematological parameters detected withoutlimitation using plethysmography, to determine a degree of hydration ofa user; degree of hydration may be associated with an ability to performunder various circumstances. For instance, a person with adequatehydration may be better able to resist the effects of hypoxemia inhigh-altitude and/or high-G for longer or under more severecircumstances, either because the person's body is better able torespond to causes of hypoxemia and delay onset, or because the person isbetter able to cope with diminished blood oxygen; this may be true ofother conditions and/or physiological states detected using at least aphysiological sensor 316, and may be detected using heuristics orrelationships derived, without limitation, using machine learning and/ordata analysis as set forth in further detail below.

Still referring to FIGS. 3A-3E, at least a physiological sensor 316 mayinclude a volatile organic compound (VOC) sensor. VOC sensor may senseVOCs, including ketones such as acetone; a user may emit ketones ingreater quantities when undergoing some forms of physiological stress,including without limitation hypoglycemia resulting from fasting oroverwork, which sometimes results in a metabolic condition known asketosis. As a result, detections of higher quantities of ketones mayindicate a high degree of exhaustion or low degree of available energy;this may be associated with a lessened ability to cope with otherphysiological conditions and/or parameters that may be detected by orusing at least a physiological sensor 316, such as hypoxemia, and/orenvironmental stressors such as high altitude or G-forces. Suchassociations may be detected or derived using data analysis and/ormachine learning as described in further detail below.

With continued reference to FIGS. 3A-3E, at least a physiologicalparameter may include neural oscillations generated by user neurons,including without limitation neural oscillations detected in the user'scranial region, sometimes referred to as “brainwaves.” Neuraloscillations include electrical or magnetic oscillations generated byneurological activity, generally of a plurality of neurons, includingsuperficial cranial neurons, thalamic pacemaker cells, or the like.Neural oscillations may include alpha waves or Berger's waves,characterized by frequencies on the order of 7.5-12.5 Hertz, beta waves,characterized by frequencies on the order of 13-30 Hertz, delta waves,having frequencies ranging from 1-4 Hertz, theta waves, havingfrequencies ranging from 4-8 Hertz, low gamma waves having frequenciesfrom 30-70 Hertz, and high gamma waves, which have frequencies from70-150 Hertz. Neurological oscillations may be associated with degreesof wakefulness, consciousness, or other neurological states of user, forinstance as described in further detail below. At least a sensor maydetect body temperature of at least a portion of user's body, using anysuitable method or component for temperature sensing.

Still referring to FIGS. 3A-3E, at least a physiological sensor 316 mayinclude an optical sensor, which detects light emitted, reflected, orpassing through human tissue. Optical sensor may include a near-infraredspectroscopy sensor (NIRS). A NIRS, as used herein, is a sensor thatdetects signals in the near-infrared electromagnetic spectrum region,having wavelengths between 780 nanometers and 2,500 nanometers. FIG. 6illustrates an exemplary embodiment of a NIRS 600 against an exteriorbody surface, which may include skin. NIRS 600 may include a lightsource 604, which may include one or more light-emitting diodes (LEDs)or similar element. Light source 604 may, as a non-limiting example,convert electric energy into near-infrared electromagnetic signals.Light source 604 may include one or more lasers. NIRS 600 may includeone or more detectors 608 configured to detect light in thenear-infrared spectrum. Although the wavelengths described herein areinfrared and near-infrared, light source 604 may alternatively oradditionally emit light in one or more other wavelengths, includingwithout limitation blue, green, ultraviolet, or other light, which maybe used to sense additional physiological parameters. In an embodiment,light source may include one or more multi-wavelength light emitters,such as one or more multi-wavelength LEDs, permitting detection ofblood-gas toxicology. Additional gases or other blood parameters sodetected may include, without limitation CO2 saturation levels, state ofhemoglobin as opposed to blood oxygen saturation generally. One or moredetectors 608 may include, without limitation, charge-coupled devices(CCDs) biased for photon detection, indium gallium arsenide (InGaAs)photodetectors, lead sulfide (PbS) photodetectors, or the like. NIRS 600may further include one or more intermediary optical elements (notshown), which may include dispersive elements such as prisms ordiffraction gratings, or the like. In an embodiment, NIRS 600 may beused to detect one or more circulatory parameters, which may include anydetectable parameter further comprises at least a circulatory parameter.At least a physiological sensor 316 may include at least two sensorsmounted on opposite sides of user's cranium.

Referring again to FIGS. 3A-3E, at least a physiological sensor 316 mayinclude a neural activity sensor. A neural activity sensor, as usedherein, includes any sensor disposed to detect electrical or magneticphenomena generated by neurons, including cranial neurons such as thoselocated in the brain or brainstem. Neural activity sensor may include anelectroencephalographic sensor. Neural activity sensor may include amagnetoencephalographic sensor. In an embodiment, neural activity sensormay be configured to detect neural oscillations. At least a sensor mayinclude an eye-tracking sensor, such as one or more cameras for trackingthe eyes of user. Eye-tracking sensor may include, as a non-limitingexample, one or more electromyographic (EMG) sensors, which may detectelectrical activity of eye muscles; electrical activity may indicateactivation of one or more eye muscles to move the eye and used by acircuit such as an alert circuit as described below to determine amovement of user's eyeball, and thus its current location of focus.

Continuing to refer to FIGS. 3A-3E, device 300 may communicate with oneor more physiological sensors that are not a part of device 300; one ormore physiological sensors may include any sensor suitable for use as atleast a physiological sensor 316 and/or any other physiological sensor.Communication with physiological sensors that are not part of device maybe accomplished by any means for wired or wireless communication betweendevices and/or components as described herein. Device may detect and/ormeasure at least a physiological parameter using any suitablecombination of at least a physiological sensor and/or physiologicalsensors that are not a part of device 300. Device 300 may combine two ormore physiological parameters to detect a physiological condition and/orphysiological alarm condition. For instance, and without limitation,where device 300 is configured to detect hypoxic incapacitation and/orone or more degrees of hypoxemia as described in further detail below,device 300 may perform such determination using a combination of heartrate and blood oxygen saturation, as detected by one or more sensor asdescribed above.

Still viewing FIGS. 3A-3E, at least a physiological sensor 316 may beattached to housing 304; attachment to housing 304 may include mountingon an exterior surface of housing 304, incorporation within housing 304,electrical connection to another element within housing 304, or thelike. Alternatively or additionally, at least a physiological sensor 316may include a sensor that is not attached to housing 304 or isindirectly attached via wiring, wireless connections, or the like. As anon-limiting example, at least a physiological sensor 316 and/or one ormore components thereof may be coupled to the pliable seal 312. In anembodiment, at least a physiological sensor 316 may be contactingexterior body surface; this may include direct contact with the exteriorbody surface, or indirect contact for instance through a portion of seal312 or other components of device 300. In an embodiment, at least aphysiological sensor 316 may contact a locus on the exterior bodysurface where substantially no muscle is located between the exteriorbody surface and an underlying bone structure, meaning muscle is notlocated between the exterior body surface and an underlying bonestructure and/or any muscle tissue located there is unnoticeable to auser as a muscle and/or incapable of appreciably flexing or changing itswidth in response to neural signals; such a locus may include, as anon-limiting example, locations on the upper cranium, forehead, nose,behind the ear, at the end of an elbow, on a kneecap, at the coccyx, orthe like. Location at a locus where muscle is not located betweenexterior body surface and underlying bone structure may decrease readinginterference and/or inaccuracies created by movement and flexing ofmuscular tissue. At least a physiological sensor 316 may contact a locushaving little or no hair on top of skin. At least a physiological sensor316 may contact a locus near to a blood vessel, such as a locus where alarge artery such as the carotid artery or a branch thereof, or a largevein such as the jugular vein, runs near to skin or bone at thelocation; in an embodiment, such a position may permit at least aphysiological sensor 316 to detect circulatory parameters as describedabove.

Still viewing FIGS. 3A-3E, processor 320 may incorporate or be incommunication with at least a user-signaling device 328. In anembodiment, at least a user-signaling device 328 may be incorporated indevice 300; for instance, at least a user-signaling device 328 may beattached to or incorporated in housing 304. Where at least auser-signaling device 328 contacts an exterior body surface of user,housing 304 may act to place at least a user-signaling device 328 incontact exterior body surface of user. Alternatively or additionally,device 300 may communicate with a user-signaling device 328 that is notincorporated in device 300, such as a display, headset, or other deviceprovided by a third party or the like, which may be in communicationwith processor 320. User-signaling device 328 may be or incorporate adevice for communication with an additional user-signaling device suchas a vehicle display and/or helmet avionics; for instance,user-signaling device 328 may include a wireless transmitter ortransponder in communication with such additional devices. In anembodiment, and without limitation, user-signaling device 328 may beconfigured to indicate a cognitive status to at least a user, asdescribed in further detail below.

Continuing to refer to FIGS. 3A-3E, at least a user-signaling device 328may include any device capable of transmitting an audible, tactile orvisual signal to a user when triggered to do so by processor 320. In anembodiment, and as a non-limiting example, at least a user-signalingdevice 328 may include a bone-conducting transducer in vibrationalcontact with a bone beneath the exterior body surface. A“bone-conducting transducer,” as used herein, is a device or componentthat bi-directionally converts an electric signal to a vibrationalsignal of a bone of a user.” In some cases, bone of user may conductvibrational signal to an inner-ear of user, which interprets thevibration as an audible signal. Alternatively or additionally, abone-conducting transducer 328 may be used detect vibrational signalsemanating from a user, for example during a user's speech.Bone-conducting transducer may include, for instance, a piezoelectricelement, which may be similar to the piezoelectric element found inspeakers or microphones, which converts an electric signal intovibrations and/or vice versa. In an embodiment, bone-conductingtransducer 128 may be mounted to housing 304 in a position placing it incontact with a user's bone; for instance, where housing 304 includes oris incorporated in an ear cup, housing 304 may place bone-conductingtransducer in contact with user's skull just behind the ear, over thesternocleidomastoid muscle. Likewise, where housing 304 includes aheadset, mask, or helmet, housing 304 may place bone-conductingtransducer in contact with a portion of user's skull that is adjacent toor covered by headset, mask, or helmet.

Still referring to FIGS. 3A-3E, at least a user-signaling device 328 mayfurther include an audio output device. Audio output device may includeany device that converts an electrical signal into an audible signal,including without limitation speakers, headsets, headphones, or thelike. As a non-limiting example, audio output device may include aheadset speaker of a headset incorporating or connected to device 300, aspeaker in a vehicle user is traveling in, or the like. At least auser-signaling device 328 may include a light output device, which maybe any device that converts an electrical signal into visible light;light output device may include one or more light source 604 s such asLEDs, as well as a display, which may be any display as described belowin reference to FIG. 10 . At least a user-signaling device 328 mayinclude a vehicular display; at least a vehicular display may be anydisplay or combination of displays presenting information to a user of avehicle user is operating. For instance, at least a vehicular displaymay include any combination of audio output devices, light outputdevices, display screens, and the like in an aircraft flight console, acar dashboard, a boat dashboard or console, or the like; processor 320may be in communication with vehicular display using any form ofcommunicative coupling described above, including without limitationwired or wireless connection. At least a user-signaling device 328 mayinclude a helmet display; helmet display may include any visual, audio,or tactile display incorporated in any kind of helmet or headgear, whichmay be in communication with processor 320 according to any form ofcommunicative coupling as described above.

Still viewing FIGS. 3A-3E, user-signaling device 328 and/or processor320 may be programmed to produce a variety of indications, which maycorrespond to various physiological alarm conditions and/or contexts.Possible indications may be, but are not limited to imminentunconsciousness, substandard oxygenation, erratic pulse, optimumoxygenation, and/or any other suitable indication, while maintaining thespirit of the present invention. Each such indication may have adistinct pattern of audible, visual, and/or textual indications; eachindication may include, for instance, an audible or textual warning ordescription of a physiological alarm condition. Any of the aboveuser-signaling devices 328 and/or signals may be used singly or incombination; for instance, a signal to user may include an audio signalproduced using a bone-conducting transducer, a verbal warning messageoutput by an audio output device, and a visual display of an image ortext indicating the physiological alarm condition. Persons skilled inthe art, upon reviewing the entirety of this disclosure, will be awareof various combinations of signaling means and/or processes that may beemployed to convey a signal to user. In an embodiment, in addition totransmitting an alarm to user-signaling device 328, alert circuit maytransmit a signal to one or more automated vehicular controls or othersystems to alleviate one or more environmental parameters contributingto physiological alarm condition. For instance, and without limitation,an automated aircraft control may receive an indication of hypoxia whilea motion sensor indicates high acceleration; aircraft control may reduceacceleration to alleviate the hypoxia. Persons skilled in the art, uponreviewing the entirety of this disclosure, may be aware of variousadditional ways in which automated systems may act to alleviate aphysiological alarm condition as described herein.

In an embodiment, and referring now to FIG. 4 , system 100 may includeor otherwise be incorporated upon a mobile respiratory device 400. A“mobile respiratory device,” as used herein, is a device worn on orabout a face of a person, which aids in respiration, for instance whenthe person is in an environment where oxygen may be scarce or whereother gases or particular matter such as carbon dioxide, carbon dioxide,toxic gases, droplets or fumes, or other elements that may interferewith respiration, and/or gases having ambient temperatures capable ofharming a person when inhaled. Such an environment may include, withoutlimitation, a cockpit of an aircraft such as a military aircraft, anartificially or naturally formed tunnel with an atmosphere that makesbreathing difficult, such as an anoxic atmosphere, an atmospherecontaining poisonous or otherwise problematic gases such as sulfurdioxide, carbon dioxide, carbon monoxide, or the like, a location at ahigh altitude such as a mountaintop, a location of a chemical spilland/or the like.

Still referring to FIG. 4 , mobile respiratory device 400 may include,without limitation, a gas mask such as a cannister mask, aself-contained breathing apparatuses (SCBA) such as those used byfirefighters, self-contained underwater breathing apparatuses (SCUBA),supplied-air respirators (SAR), particulate respirators, chemicalcartridge respirators, powered air-purifying respirators (PAPRs),respirators included as part of a protective suit, airline respirators,N-95 or other NIOSH approved respirators, and/or other devices worn onand/or over and at least partially occluding the face to aid inrespiration.

With continued reference to FIG. 4 , an “exhaust port,” as used in thisdisclosure, is an outlet that permits air exhaled by a user to escapefrom a mobile respiratory device 400. Exhaust port may include a valvesuch as a check-valve or other one-way valve to prevent air fromentering a mobile respiratory device 400 from environment. Exhaust portmay include, for instance, an exhale valve of a respirator mask or othersuch design. Exhaust port may also be an inlet port; for instance, airmay be filtered while breathing in through the port and then exhaled,with or without filtering, via a valve at the same port.

With continued reference to FIG. 4 , in some cases, mobile respiratorydevice 400 may include or house at least a sensor 404 a-b. At least asensor 404 a-b may include any sensor described in this disclosure,including for instance EMG sensors/electrodes described in reference toFIG. 2 . In some cases, mobile respiratory device 400 may allow forplacement of at least an EMG electrode 404 a-b proximal an eye of auser. In some cases, fewer than all EMG electrodes of a system 100 maybe housed within a mobile respiratory device 400. In some cases, forinstance, some EMG electrodes may be located within a mobile respiratorydevice 400 and some may be located within an earcup, as shown in FIGS.3A-3E. Placement of EMG electrodes 404 a-b at different locations abouteye may allow for more meaningful signals to be generated, for examplefor time difference of arrival analysis.

Referring now to FIG. 5 , an exemplary embodiment of a machine-learningmodule 500 that may perform one or more machine-learning processes asdescribed in this disclosure is illustrated. Machine-learning module mayperform determinations, classification, and/or analysis steps, methods,processes, or the like as described in this disclosure using machinelearning processes. A “machine learning process,” as used in thisdisclosure, is a process that automatedly uses training data 504 togenerate an algorithm that will be performed by a computingdevice/module to produce outputs 508 given data provided as inputs 512;this is in contrast to a non-machine learning software program where thecommands to be executed are determined in advance by a user and writtenin a programming language.

Still referring to FIG. 5 , “training data,” as used herein, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data 504 may include aplurality of data entries, each entry representing a set of dataelements that were recorded, received, and/or generated together; dataelements may be correlated by shared existence in a given data entry, byproximity in a given data entry, or the like. Multiple data entries intraining data 504 may evince one or more trends in correlations betweencategories of data elements; for instance, and without limitation, ahigher value of a first data element belonging to a first category ofdata element may tend to correlate to a higher value of a second dataelement belonging to a second category of data element, indicating apossible proportional or other mathematical relationship linking valuesbelonging to the two categories. Multiple categories of data elementsmay be related in training data 504 according to various correlations;correlations may indicate causative and/or predictive links betweencategories of data elements, which may be modeled as relationships suchas mathematical relationships by machine-learning processes as describedin further detail below. Training data 504 may be formatted and/ororganized by categories of data elements, for instance by associatingdata elements with one or more descriptors corresponding to categoriesof data elements. As a non-limiting example, training data 504 mayinclude data entered in standardized forms by persons or processes, suchthat entry of a given data element in a given field in a form may bemapped to one or more descriptors of categories. Elements in trainingdata 504 may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training data504 may be provided in fixed-length formats, formats linking positionsof data to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),JavaScript Object Notation (JSON), or the like, enabling processes ordevices to detect categories of data.

Alternatively or additionally, and continuing to refer to FIG. 5 ,training data 504 may include one or more elements that are notcategorized; that is, training data 504 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 504 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 504 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailbelow. Training data 504 used by machine-learning module 500 maycorrelate any input data as described in this disclosure to any outputdata as described in this disclosure. As a non-limiting illustrativeexample one or more eye patterns and speech patterns may be used asinputs and a cognitive status may be used as a correlated output.

Further referring to FIG. 5 , training data may be filtered, sorted,and/or selected using one or more supervised and/or unsupervisedmachine-learning processes and/or models as described in further detailbelow; such models may include without limitation a training dataclassifier 516. Training data classifier 516 may include a “classifier,”which as used in this disclosure is a machine-learning model as definedbelow, such as a mathematical model, neural net, or program generated bya machine learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs into categories orbins of data, outputting the categories or bins of data and/or labelsassociated therewith. A classifier may be configured to output at leasta datum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. Machine-learning module 500 may generate aclassifier using a classification algorithm, defined as a processeswhereby a computing device and/or any module and/or component operatingthereon derives a classifier from training data 504. Classification maybe performed using, without limitation, linear classifiers such aswithout limitation logistic regression and/or naive Bayes classifiers,nearest neighbor classifiers such as k-nearest neighbors classifiers,support vector machines, least squares support vector machines, fisher'slinear discriminant, quadratic classifiers, decision trees, boostedtrees, random forest classifiers, learning vector quantization, and/orneural network-based classifiers. As a non-limiting example, trainingdata classifier 516 may classify elements of training data to context.For instance, in some cases, eye and or speech parameters or patternsare likely to change dependent upon context. An emergent situation islikely to increase speech tempo, for instance. In some cases, trainingdata may be classified according to context or circumstance. Forinstance, in some cases, training data may be representative ofparameters or patterns related to behavioral phenomena in a restingstate; alternatively training data may be classified as relating to anemergent or active state. In some cases, training data may be user orcohort specific. For example, in some cases, determination of cognitivestatus from sensed behavioral phenomenon may be based upon a relativechange in behavior of a user (i.e., user is not acting like-herself).

Still referring to FIG. 5 , machine-learning module 500 may beconfigured to perform a lazy-learning process 520 and/or protocol, whichmay alternatively be referred to as a “lazy loading” or“call-when-needed” process and/or protocol, may be a process wherebymachine learning is conducted upon receipt of an input to be convertedto an output, by combining the input and training set to derive thealgorithm to be used to produce the output on demand. For instance, aninitial set of simulations may be performed to cover an initialheuristic and/or “first guess” at an output and/or relationship. As anon-limiting example, an initial heuristic may include a ranking ofassociations between inputs and elements of training data 504. Heuristicmay include selecting some number of highest-ranking associations and/ortraining data 504 elements. Lazy learning may implement any suitablelazy learning algorithm, including without limitation a K-nearestneighbors algorithm, a lazy naïve Bayes algorithm, or the like; personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various lazy-learning algorithms that may be applied togenerate outputs as described in this disclosure, including withoutlimitation lazy learning applications of machine-learning algorithms asdescribed in further detail below.

Alternatively or additionally, and with continued reference to FIG. 5 ,machine-learning processes as described in this disclosure may be usedto generate machine-learning models 524. A “machine-learning model,” asused in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above and stored in memory; an inputis submitted to a machine-learning model 524 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model 524 may be generated by creating an artificialneural network, such as a convolutional neural network comprising aninput layer of nodes, one or more intermediate layers, and an outputlayer of nodes. Connections between nodes may be created via the processof “training” the network, in which elements from a training data 504set are applied to the input nodes, a suitable training algorithm (suchas Levenberg-Marquardt, conjugate gradient, simulated annealing, orother algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning.

Still referring to FIG. 5 , machine-learning algorithms may include atleast a supervised machine-learning process 528. At least a supervisedmachine-learning process 528, as defined herein, include algorithms thatreceive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayinclude eye patterns and/or speech patterns as described above asinputs, cognitive statuses as outputs, and a scoring functionrepresenting a desired form of relationship to be detected betweeninputs and outputs; scoring function may, for instance, seek to maximizethe probability that a given input and/or combination of elements inputsis associated with a given output to minimize the probability that agiven input is not associated with a given output. Scoring function maybe expressed as a risk function representing an “expected loss” of analgorithm relating inputs to outputs, where loss is computed as an errorfunction representing a degree to which a prediction generated by therelation is incorrect when compared to a given input-output pairprovided in training data 504. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variouspossible variations of at least a supervised machine-learning process528 that may be used to determine relation between inputs and outputs.Supervised machine-learning processes may include classificationalgorithms as defined above.

Further referring to FIG. 5 , machine learning processes may include atleast an unsupervised machine-learning processes 532. An unsupervisedmachine-learning process, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 5 , machine-learning module 500 may be designedand configured to create a machine-learning model 524 using techniquesfor development of linear regression models. Linear regression modelsmay include ordinary least squares regression, which aims to minimizethe square of the difference between predicted outcomes and actualoutcomes according to an appropriate norm for measuring such adifference (e.g. a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g. a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 5 , machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminate analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms.

Machine-learning algorithms may include various forms of latent spaceregularization such as variational regularization. Machine-learningalgorithms may include Gaussian processes such as Gaussian ProcessRegression. Machine-learning algorithms may include cross-decompositionalgorithms, including partial least squares and/or canonical correlationanalysis. Machine-learning algorithms may include naïve Bayes methods.Machine-learning algorithms may include algorithms based on decisiontrees, such as decision tree classification or regression algorithms.Machine-learning algorithms may include ensemble methods such as baggingmeta-estimator, forest of randomized tress, AdaBoost, gradient treeboosting, and/or voting classifier methods. Machine-learning algorithmsmay include neural net algorithms, including convolutional neural netprocesses.

Referring now to FIG. 6 an exemplary embodiment of neural network 600 isillustrated. Neural network also known as an artificial neural network,is a network of “nodes,” or data structures having one or more inputs,one or more outputs, and a function determining outputs based on inputs.Such nodes may be organized in a network, such as without limitation aconvolutional neural network, including an input layer of nodes 604, oneor more intermediate layers 608, and an output layer of nodes 612.Connections between nodes may be created via the process of “training”the network, in which elements from a training dataset are applied toinput nodes 604, a suitable training algorithm (such asLevenberg-Marquardt, conjugate gradient, simulated annealing, or otheralgorithms) is then used to adjust the connections and weights betweennodes in adjacent layers 608 of the neural network to produce thedesired values at output nodes 612. This process is sometimes referredto as deep learning.

Referring now to FIG. 7 , an exemplary embodiment of a node 700 of aneural network is illustrated. A node 700 may include, withoutlimitation a plurality of inputs x_(i) that may receive numerical valuesfrom inputs to a neural network containing the node and/or from othernodes. Node 1000 may perform a weighted sum of inputs using weightsw_(i) that are multiplied by respective inputs x_(i). Additionally oralternatively, a bias b may be added to the weighted sum of the inputssuch that an offset is added to each unit in the neural network layerthat is independent of the input to the layer. The weighted sum may thenbe input into a function φ, which may generate one or more outputs y.Weight w_(i) applied to an input x_(i) may indicate whether the input is“excitatory,” indicating that it has strong influence on the one or moreoutputs y, for instance by the corresponding weight having a largenumerical value, and/or a “inhibitory,” indicating it has a weak effectinfluence on the one more inputs y, for instance by the correspondingweight having a small numerical value. The values of weights w_(i) maybe determined by training a neural network using training data, whichmay be performed using any suitable process as described above.

Still referring to FIG. 7 , a neural network may receive one or more ofat least an environmental parameter, at least a circulation parameter,and/or at least a respiration parameter as inputs and output one or moreof a condition and/or an imminent loss of consciousness event.Alternatively or additionally in some cases, a neural network mayreceive one or more of at least an environmental parameter, at least acirculation parameter, and/or at least a respiration parameter as inputsand output a confidence metric representing a probability ofclassification to a predetermined class, for instance condition and/orimminent loss of consciousness event, according to weights w_(i) thatare derived using machine-learning processes as described in thisdisclosure.

Referring again to FIG. 1 , In some embodiments, processor 104 may beconfigured to modify a training set in response to an individual user oranother context. For example, processor 104 may, in some cases, retraina machine-learning model using one or more of speech pattern and/or eyepattern correlated to cognitive status. In some embodiments, processor104 may be configured to classify a cognitive status and determine aconfidence metric. For example, in some exemplary embodiments confidencemetric may be a floating-point number within a prescribed range, such aswithout limitation 0 to 1, with each end of the prescribed rangerepresenting an extreme representation, such as without limitationsubstantially no confidence and substantially absolute confidence,respectively. In some cases, confidence output may represent arelationship between a result of filtering and/or classifying.Confidence metric may be determined by one more comparisons algorithms,such as without limitation a fuzzy set comparison. For example, in someexemplary embodiments a fuzzy set comparison may be employed to comparea pattern or parameter with a membership function derived to representat least a threshold used for classification, for instance of acognitive status.

Referring to FIG. 8 , an exemplary embodiment of fuzzy set comparison800 is illustrated. A first fuzzy set 804 may be represented, withoutlimitation, according to a first membership function 808 representing aprobability that an input falling on a first range of values 812 is amember of the first fuzzy set 804, where the first membership function808 has values on a range of probabilities such as without limitationthe interval [0,1], and an area beneath the first membership function808 may represent a set of values within first fuzzy set 804. Althoughfirst range of values 812 is illustrated for clarity in this exemplarydepiction as a range on a single number line or axis, first range ofvalues 812 may be defined on two or more dimensions, representing, forinstance, a Cartesian product between a plurality of ranges, curves,axes, spaces, dimensions, or the like. First membership function 808 mayinclude any suitable function mapping first range 812 to a probabilityinterval, including without limitation a triangular function defined bytwo linear elements such as line segments or planes that intersect at orbelow the top of the probability interval. As a non-limiting example,triangular membership function may be defined as:

${y( {x,a,b,c} )} = \{ \begin{matrix}{0,\ {{{for}{\ }x} > {c{and}x} < a}} \\{\frac{x - a}{b - a},{{{for}a} \leq x < b}} \\{\frac{c - x}{c - b},\ {{{if}b} < x \leq c}}\end{matrix} $a trapezoidal membership function may be defined as:

${y( {x,a,b,c,d} )} = {\max( {{\min\ ( {\frac{x - a}{b - a},\ 1,\frac{d - x}{d - c}} )}\ ,\ 0} )}$a sigmoidal function may be defined as:

${y( {x,a,c} )} = \frac{1}{1 - e^{- {a({x - c})}}}$a Gaussian membership function may be defined as:

${y( {x,c,\sigma} )} = e^{\frac{1}{2}{(\frac{x - c}{\sigma})}^{2}}$and a bell membership function may be defined as:

${y( {x,a,b,c,} )} = \lbrack {1 + {❘\frac{x - c}{a}❘}^{2b}} \rbrack^{- 1}$Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various alternative or additionalmembership functions that may be used consistently with this disclosure.

Still referring to FIG. 8 , first fuzzy set 804 may represent any valueor combination of values as described above, including output from oneor more algorithms, one or more machine-learning models, one or moresensors, and a predetermined class, such as without limitation a speechpattern, eye pattern, and/or cognitive status. A second fuzzy set 816,which may represent any value which may be represented by first fuzzyset 804, may be defined by a second membership function 820 on a secondrange 824; second range 824 may be identical and/or overlap with firstrange 812 and/or may be combined with first range via Cartesian productor the like to generate a mapping permitting evaluation overlap of firstfuzzy set 804 and second fuzzy set 816. Where first fuzzy set 804 andsecond fuzzy set 816 have a region 828 that overlaps, first membershipfunction 808 and second membership function 820 may intersect at a point832 representing a probability, as defined on probability interval, of amatch between first fuzzy set 804 and second fuzzy set 816.Alternatively or additionally, a single value of first and/or secondfuzzy set may be located at a locus 836 on first range 812 and/or secondrange 824, where a probability of membership may be taken by evaluationof first membership function 808 and/or second membership function 820at that range point. A probability at 828 and/or 832 may be compared toa threshold 840 to determine whether a positive match is indicated.Threshold 840 may, in a non-limiting example, represent a degree ofmatch between first fuzzy set 804 and second fuzzy set 816, and/orsingle values therein with each other or with either set, which issufficient for purposes of the matching process; for instance, thresholdmay indicate a sufficient degree of overlap between an output from oneor more machine-learning models and/or sensors and a predeterminedclass, such as without limitation a cognitive state, for combination tooccur as described above. Alternatively or additionally, each thresholdmay be tuned by a machine-learning and/or statistical process, forinstance and without limitation as described in further detail below.

Further referring to FIG. 8 , in an embodiment, a degree of matchbetween fuzzy sets may be used to classify one or more of at least aneye pattern and at least a speech pattern to a cognitive status. Forinstance, if one or more of at least an eye pattern and at least aspeech pattern has a fuzzy set matching a cognitive status fuzzy set byhaving a degree of overlap exceeding a threshold, processor 104 mayclassify the one or more of at least an eye pattern and at least aspeech pattern as belonging to the cognitive status. Where multiplefuzzy matches are performed, degrees of match for each respective fuzzyset may be computed and aggregated through, for instance, addition,averaging, or the like, to determine an overall degree of match.

Still referring to FIG. 8 , in an embodiment, one or more of at least aneye pattern and at least a speech pattern may be compared to multiplecognitive status fuzzy sets. For instance, one or more of at least aneye pattern and at least a speech pattern may be represented by a fuzzyset that is compared to each of the multiple cognitive status fuzzysets; and a degree of overlap exceeding a threshold between the one ormore of at least an eye pattern and the at least a speech pattern andany of the multiple cognitive status fuzzy sets may cause processor 104to classify the one or more of at least an eye pattern and at least aspeech pattern as belonging to a cognitive status. For instance, in oneembodiment there may be two cognitive status fuzzy sets, representingrespectively unimpaired and impaired cognitive performance. Impairedcognitive status may have an impaired fuzzy set; unimpaired cognitivestatus may have an unimpaired fuzzy set; and one or more of at least aneye pattern and at least a speech pattern may have a pattern fuzzy set.Processor 104, for example, may compare a pattern fuzzy set with each ofimpaired fuzzy set and unimpaired fuzzy set, as described above, andclassify one or more of at least an eye pattern and at least a speechpattern to either, both, or neither of impaired or unimpaired cognitivestatuses. Machine-learning methods as described throughout may, in anon-limiting example, generate coefficients used in fuzzy set equationsas described above, such as without limitation x, c, and σ of a Gaussianset as described above, as outputs of machine-learning methods.Likewise, one or more of at least an eye pattern, at least a speechpattern, at least an eye parameter, and at least a speech parameter maybe used indirectly to determine a fuzzy set, as pattern fuzzy set may bederived from outputs of one or more machine-learning models and/oralgorithms that take the aforementioned patterns and/or parameters asinputs.

Referring now to FIG. 9 , an exemplary method 900 of determining pilotstatus according to behavioral phenomenon is illustrated by way of aflow diagram. At step 905, method 900 may include detecting, using atleast an eye sensor, at least an eye parameter as a function of at leastan eye phenomenon. Eye sensor may include any sensor described in thisdisclosure, including with reference to FIGS. 1-8 . Eye parameter mayinclude any eye parameter described in this disclosure, including withreference to FIGS. 1-8 . Eye phenomenon may include any eye phenomenondescribed in this disclosure, including with reference to FIGS. 1-8 . Insome embodiments, at least an eye sensor may include an electromyographysensor. In some cases, an electromyography sensor may be configured todetect at least an eye parameter as a function of at least an eyephenomenon. Electromyography sensor may include any electromyographysensor described in this disclosure, including with reference to FIGS.1-8 . In some embodiments, at least an eye sensor may include an opticalsensor. In some cases, optical sensor may be configured to detect atleast an eye parameter as a function of at least an eye phenomenon.Optical sensor may include any optical sensor described in thisdisclosure, including with reference to FIGS. 1-8 .

With continued reference to FIG. 9 , at step 910, method 900 may includedetecting, using at least a speech sensor, at least a speech parameteras a function of at least a speech phenomenon. Speech sensor may includeany sensor described in this disclosure, including with reference toFIGS. 1-8 . Speech parameter may include any speech parameter describedin this disclosure, including with reference to FIGS. 1-8 . Speechphenomenon may include any speech phenomenon described in thisdisclosure, including with reference to FIGS. 1-8 . In some embodiments,at least a speech sensor may include a bone conductance transducer. Insome cases, bone conductance transducer may be configured to detect atleast a speech parameter as a function of at least a speech phenomenon.Bone conductance transducer may include any bone conductance transducerdescribed in this disclosure, including with reference to FIGS. 1-8 . Insome embodiments, at least a speech sensor may include a microphone. Insome cases, microphone may be configured to detect at least a speechparameter as a function of at least a speech phenomenon. Microphone mayinclude any microphone described in this disclosure, including withreference to FIGS. 1-8 .

With continued reference to FIG. 9 , at step 915, method 900 may includereceiving, using a processor in communication with at least an eyesensor and at least a speech sensor, at least an eye parameter and atleast a speech parameter. Processor may include any processor and/orcomputing device described in this disclosure, including with referenceto FIGS. 1-8 and 10 .

With continued reference to FIG. 9 , at step 920, method 900 may includedetermining, using processor, at least an eye pattern as a function ofat least an eye parameter. Eye pattern may include any eye patterndescribed in this disclosure, including with reference to FIGS. 1-8 . Insome embodiments, step 920 may additionally include receiving an eyepattern training data, inputting the eye pattern training data into aneye pattern machine learning algorithm, training an eye pattern machinelearning model, as a function of the eye pattern machine learningalgorithm, and determining the at least an eye pattern as a function ofthe eye pattern machine learning model and the at least an eyeparameter. Eye pattern training data may include any training datadescribed in this disclosure, including with reference to FIGS. 1-8 .Eye pattern machine learning algorithm may include any machine learningalgorithm described in this disclosure, including with reference toFIGS. 1-8 . Eye pattern machine learning model may include any machinelearning model described in this disclosure, including with reference toFIGS. 1-8 .

With continued reference to FIG. 9 , at step 925 method 900 may includedetermining, using processor, at least a speech pattern as a function ofat least a speech parameter. Speech pattern may include any speechpattern described in this disclosure, including with reference to FIGS.1-8 . In some embodiments, step 925 may additionally include receiving aspeech pattern training data, inputting the speech pattern training datainto a speech pattern machine learning algorithm, training a speechpattern machine learning model, as a function of the speech patternmachine learning algorithm, and determining the at least a speechpattern as a function of the speech pattern machine learning model andat least a speech parameter. Speech pattern training data may includeany training data described in this disclosure, including with referenceto FIGS. 1-8 . Speech pattern machine learning algorithm may include anymachine learning algorithm described in this disclosure, including withreference to FIGS. 1-8 . Speech pattern machine learning model mayinclude any machine learning model described in this disclosure,including with reference to FIGS. 1-8 .

With continued reference to FIG. 9 , at step 930, method 900 may includecorrelating, using processor, one or more of at least an eye pattern andat least a speech pattern to a cognitive status. Cognitive status mayinclude any cognitive status described in this disclosure, includingwith reference to FIGS. 1-8 . In some embodiments, step 930 mayadditionally include receiving a cognitive status training data,inputting the cognitive status training data into a cognitive statusmachine learning algorithm, training a cognitive status machine learningmodel, as a function of the cognitive status machine learning algorithm,and determining the cognitive status as a function of the cognitivestatus machine learning model and one or more of the at least an eyepattern and the at least a speech pattern. Cognitive status trainingdata may include any training data described in this disclosure,including with reference to FIGS. 1-8 . Cognitive status machinelearning algorithm may include any machine learning algorithm describedin this disclosure, including with reference to FIGS. 1-8 . Cognitivestatus machine learning model may include any machine learning modeldescribed in this disclosure, including with reference to FIGS. 1-8 .

Still referring to FIG. 9 , in some embodiments, method 900 mayadditionally include correlating, using processor, one or more of atleast an eye parameter and at least a speech parameter to cognitivestatus.

Still referring to FIG. 9 , in some embodiments, method 900 mayadditionally include determining, using processor, a confidence metricassociated with correlation to cognitive status. Confidence metric mayinclude any confidence metric described in this disclosure, includingwith reference to FIGS. 1-8 .

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 10 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 1000 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 1000 includes a processor 1004 and a memory1008 that communicate with each other, and with other components, via abus 1012. Bus 1012 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Processor 1004 may include any suitable processor, such as withoutlimitation a processor incorporating logical circuitry for performingarithmetic and logical operations, such as an arithmetic and logic unit(ALU), which may be regulated with a state machine and directed byoperational inputs from memory and/or sensors; processor 1004 may beorganized according to Von Neumann and/or Harvard architecture as anon-limiting example. Processor 1004 may include, incorporate, and/or beincorporated in, without limitation, a microcontroller, microprocessor,digital signal processor (DSP), Field Programmable Gate Array (FPGA),Complex Programmable Logic Device (CPLD), Graphical Processing Unit(GPU), general purpose GPU, Tensor Processing Unit (TPU), analog ormixed signal processor, Trusted Platform Module (TPM), a floating pointunit (FPU), and/or system on a chip (SoC).

Memory 1008 may include various components (e.g., machine-readablemedia) including, but not limited to, a random-access memory component,a read only component, and any combinations thereof. In one example, abasic input/output system 1016 (BIOS), including basic routines thathelp to transfer information between elements within computer system1000, such as during start-up, may be stored in memory 1008. Memory 1008may also include (e.g., stored on one or more machine-readable media)instructions (e.g., software) 1020 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 1008 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 1000 may also include a storage device 1024. Examples ofa storage device (e.g., storage device 1024) include, but are notlimited to, a hard disk drive, a magnetic disk drive, an optical discdrive in combination with an optical medium, a solid-state memorydevice, and any combinations thereof. Storage device 1024 may beconnected to bus 1012 by an appropriate interface (not shown). Exampleinterfaces include, but are not limited to, SCSI, advanced technologyattachment (ATA), serial ATA, universal serial bus (USB), IEEE 1394(FIREWIRE), and any combinations thereof. In one example, storage device1024 (or one or more components thereof) may be removably interfacedwith computer system 1000 (e.g., via an external port connector (notshown)). Particularly, storage device 1024 and an associatedmachine-readable medium 1028 may provide nonvolatile and/or volatilestorage of machine-readable instructions, data structures, programmodules, and/or other data for computer system 1000. In one example,software 1020 may reside, completely or partially, withinmachine-readable medium 1028. In another example, software 1020 mayreside, completely or partially, within processor 1004.

Computer system 1000 may also include an input device 1032. In oneexample, a user of computer system 1000 may enter commands and/or otherinformation into computer system 1000 via input device 1032. Examples ofan input device 1032 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 1032may be interfaced to bus 1012 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 1012, and any combinations thereof. Input device 1032may include a touch screen interface that may be a part of or separatefrom display 1036, discussed further below. Input device 1032 may beutilized as a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 1000 via storage device 1024 (e.g., a removable disk drive, aflash drive, etc.) and/or network interface device 1040. A networkinterface device, such as network interface device 1040, may be utilizedfor connecting computer system 1000 to one or more of a variety ofnetworks, such as network 1044, and one or more remote devices 1048connected thereto. Examples of a network interface device include, butare not limited to, a network interface card (e.g., a mobile networkinterface card, a LAN card), a modem, and any combination thereof.Examples of a network include, but are not limited to, a wide areanetwork (e.g., the Internet, an enterprise network), a local areanetwork (e.g., a network associated with an office, a building, a campusor other relatively small geographic space), a telephone network, a datanetwork associated with a telephone/voice provider (e.g., a mobilecommunications provider data and/or voice network), a direct connectionbetween two computing devices, and any combinations thereof. A network,such as network 1044, may employ a wired and/or a wireless mode ofcommunication. In general, any network topology may be used. Information(e.g., data, software 1020, etc.) may be communicated to and/or fromcomputer system 1000 via network interface device 1040.

Computer system 1000 may further include a video display adapter 1052for communicating a displayable image to a display device, such asdisplay device 1036. Examples of a display device include, but are notlimited to, a liquid crystal display (LCD), a cathode ray tube (CRT), aplasma display, a light emitting diode (LED) display, and anycombinations thereof. Display adapter 1052 and display device 1036 maybe utilized in combination with processor 1004 to provide graphicalrepresentations of aspects of the present disclosure. In addition to adisplay device, computer system 1000 may include one or more otherperipheral output devices including, but not limited to, an audiospeaker, a printer, and any combinations thereof. Such peripheral outputdevices may be connected to bus 1012 via a peripheral interface 1056.Examples of a peripheral interface include, but are not limited to, aserial port, a USB connection, a FIREWIRE connection, a parallelconnection, and any combinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,systems, and software according to the present disclosure. Accordingly,this description is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. A system for determining actor status accordingto behavioral phenomenon, the system comprising: at least a speechsensor configured to detect at least a speech parameter as a function ofat least a speech phenomenon; at least an eye sensor configured todetect at least an eye parameter as a function of at least an eyephenomenon; and a processor in communication with the at least an eyesensor and the at least a speech sensor and configured to: receive theat least a speech parameter; determine at least a speech pattern as afunction of the at least a speech parameter; determine at least an eyepattern as a function of the at least an eye parameter; and correlatethe at least a speech pattern and the at least an eye pattern to acognitive status, wherein correlating to the cognitive status furthercomprises: receiving cognitive status training data comprising the atleast a speech pattern, the at least an eye pattern, and historicinformation; training a cognitive status machine learning model, as afunction of the cognitive status training data; and determining thecognitive status as a function of the cognitive status machine learningmodel.
 2. The system of claim 1, wherein the processor is furtherconfigured to determine a confidence metric associated with thecorrelation to the cognitive status.
 3. The system of claim 1, whereindetermining the at least a speech pattern comprises: receiving a speechpattern training data; inputting the speech pattern training data into aspeech pattern machine learning algorithm; training a speech patternmachine learning model, as a function of the speech pattern machinelearning algorithm; and determining the at least a speech pattern as afunction of the speech pattern machine learning model and the at least aspeech parameter.
 4. The system of claim 1, wherein determining the atleast an eye pattern comprises: receiving an eye pattern training data;inputting the eye pattern training data into an eye pattern machinelearning algorithm; training an eye pattern machine learning model, as afunction of the eye pattern machine learning algorithm; and determiningthe at least an eye pattern as a function of the eye pattern machinelearning model and the at least an eye parameter.
 5. The system of claim1, wherein correlating to the cognitive status further comprises:receiving a cognitive status training data; inputting the cognitivestatus training data into a cognitive status machine learning algorithm;training a cognitive status machine learning model, as a function of thecognitive status machine learning algorithm; and determining thecognitive status as a function of the cognitive status machine learningmodel and one or more of the at least an eye pattern and the at least aspeech pattern.
 6. The system of claim 1, wherein the at least an eyesensor comprises an electromyography sensor configured to detect the atleast an eye parameter as a function of the at least an eye phenomenon.7. The system of claim 1, wherein the at least an eye sensor comprisesan optical sensor configured to detect the at least an eye parameter asa function of the at least an eye phenomenon.
 8. The system of claim 1,wherein the at least a speech sensor comprises a bone conductancetransducer configured to detect the at least a speech parameter as afunction of the at least a speech phenomenon.
 9. The system of claim 1,wherein the at least a speech sensor comprises a microphone configuredto detect the at least a speech parameter as a function of the at leasta speech phenomenon.
 10. A method of determining actor status accordingto behavioral phenomena, the method comprising: detecting, using atleast a speech sensor, at least a speech parameter as a function of atleast a speech phenomenon; detecting, using at least and eye sensor, atleast an eye parameter as a function of at least an eye phenomenon;receiving, using a processor in communication with the at least a speechsensor, the at least a speech parameter; determining, using theprocessor, at least a speech pattern as a function of the at least aspeech parameter; determining, using the processor, at least an eyepattern as a function of the at least an eye parameter; and correlating,using the processor, the at least a speech pattern and the at least aneye pattern to a cognitive status, wherein correlating to the cognitivestatus further comprises: receiving cognitive status training datacomprising the at least a speech pattern, the at least an eye pattern,and historic information; training a cognitive status machine learningmodel, as a function of the cognitive status training data; anddetermining the cognitive status as a function of the cognitive statusmachine learning model.
 11. The method of claim 10, further comprisingdetermining, using the processor, a confidence metric associated withthe correlation to the cognitive status.
 12. The method of claim 10,wherein determining the at least a speech pattern comprises: receiving aspeech pattern training data; inputting the speech pattern training datainto a speech pattern machine learning algorithm; training a speechpattern machine learning model, as a function of the speech patternmachine learning algorithm; and determining the at least a speechpattern as a function of the speech pattern machine learning model andthe at least a speech parameter.
 13. The method of claim 10, whereindetermining the at least an eye pattern comprises: receiving an eyepattern training data; inputting the eye pattern training data into aneye pattern machine learning algorithm; training an eye pattern machinelearning model, as a function of the eye pattern machine learningalgorithm; and determining the at least an eye pattern as a function ofthe eye pattern machine learning model and the at least an eyeparameter.
 14. The method of claim 10, wherein correlating to thecognitive status further comprises: receiving a cognitive status eyepattern training data; inputting the cognitive status training data intoa cognitive status machine learning algorithm; training a cognitivestatus machine learning model, as a function of the cognitive statusmachine learning algorithm; and determining the cognitive status as afunction of the cognitive status machine learning model and one or moreof the at least an eye pattern and the at least a speech pattern. 15.The method of claim 10, wherein the at least an eye sensor comprises anelectromyography sensor configured to detect the at least an eyeparameter as a function of the at least an eye phenomenon.
 16. Themethod of claim 10, wherein the at least an eye sensor comprises anoptical sensor configured to detect the at least an eye parameter as afunction of the at least an eye phenomenon.
 17. The method of claim 10,wherein the at least a speech sensor comprises a bone conductancetransducer configured to detect the at least a speech parameter as afunction of the at least a speech phenomenon.
 18. The method of claim10, wherein the at least a speech sensor comprises a microphoneconfigured to detect the at least a speech parameter as a function ofthe at least a speech phenomenon.